class_docstr: A wrapper around torchvision transforms. It applies specific transform
  to ``img`` and updates ``height`` and ``width`` accordingly.
  
  Required Keys:
  
  - img (ndarray): The input image.
  
  Modified Keys:
  
  - img (ndarray): The modified image.
  - img_shape (tuple(int, int)): The shape of the image in (height, width).
  
  
  Warning:
      This transform only affects the image but not its associated
      annotations, such as word bounding boxes and polygons. Therefore,
      it may only be applicable to text recognition tasks.
  
  Args:
      op (str): The name of any transform class in
          :func:`torchvision.transforms`.
      **kwargs: Arguments that will be passed to initializer of torchvision
          transform.
code: def __init__(self, op: str, **kwargs) -> None:
      assert isinstance(op, str)
      obj_cls = getattr(torchvision_transforms, op)
      self.torchvision = obj_cls(**kwargs)
      self.op = op
      self.kwargs = kwargs
  
imports: import warnings
  from typing import Any, Dict, List, Optional, Tuple, Union
  import imgaug
  import imgaug.augmenters
  import numpy
  import torchvision.transforms
  from mmcv.transforms import Compose
  from mmcv.transforms.base import BaseTransform
  from PIL import Image
  from mmocr.registry import TRANSFORMS
  from mmocr.utils import poly2bbox
label_desc: Use image node_id 1 to create a new TorchVisionWrapper object from inherited base classes: BaseTransform with example: obj = TorchVisionWrapper(op)